arXiv AI
Jun 16
Action with Visual Primitives
arXiv:2605. 22183v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation.
Bring My Cup! Personalizing Vision-Language-Action Models with Visual Attentive Prompting
arXiv:2512. 20014v3 Announce Type: replace-cross Abstract: While Vision-Language-Action (VLA) models generalize well to generic instructions, they struggle with personalized commands such as "bring my cup," where the robot must act on one specific instance among visually similar objects.
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arXiv AI
Jul 3
Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration
arXiv:2603. 06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies.
arXiv Computer Vision
3d ago
Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance
arXiv:2609.38616v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including ma...
arXiv Machine Learning
Jun 16
AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
arXiv:2511. 18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep.
arXiv Computer Vision
Sep 3
Towards Zero-Shot Transfer Across Embodiments For Driving VLAs
The paper investigates how Vision‑Language‑Action (VLA) models can generalise across different driving environments and camera setups. It introduces a multi‑dataset training strategy and an auxiliary objective called BEV‑Forcing, which injects bird‑eye‑view spatial information into the VLA backbone to improve both in‑distribution and out‑of‑distribution performance on a limited number of camera rigs. The authors observe that while BEV‑Forcing helps when training data is scarce, its advantage diminishes as the number of training embodiments grows, suggesting that scaling diversity may reduce the impact of such auxiliary tasks.
arXiv Machine Learning
Jun 11
Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.
arXiv AI
Sep 18
JEPA-WAM: Connecting Generated Visual Instructions to World Action Models through JEPA Latent Representations
JEPA-WAM enhances World Action Models (WAMs) by pairing text instructions with stochastically generated visual cues, using a text-to-image generator and a frozen V‑JEPA encoder to create dense goal representations. These representations are compressed into goal tokens that condition both video and action experts via cross‑attention, enabling the model to better ground instructions. On a new real‑robot benchmark, JEPA‑WAM attains 87.3%, 74.5%, and 80.9% success rates across in‑distribution, out‑of‑distribution scenes, and out‑of‑distribution instructions, outperforming prior methods by significant margins.
arXiv Computer Vision
Aug 27
V-Link: Recovering Lost Visual Representations in Action DiT for Vision-Language-Action Models
V-Link is a method designed to enhance Vision‑Language‑Action (VLA) models by recovering visual representations during the transfer from vision‑language (VL) features to action (A) features. It introduces complementary Spatial and Semantic Query representations that are injected into Action DiT through asymmetric pathways, providing both semantic augmentation and dedicated geometric conditioning for action generation. Experiments on LIBERO, LIBERO‑Plus, RoboTwin 2.0, and real‑world AGIBOT A3 Ultra tasks show significant performance gains over the base GR00T N1.6 model.
arXiv Computer Vision
Sep 22
AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
arXiv:2609.22332v1 Announce Type: cross Abstract: Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act...
arXiv Machine Learning
Jun 10
Task Robustness via Re-Labelling Vision-Action Robot Data
arXiv:2606. 10918v1 Announce Type: cross Abstract: The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios.
arXiv Computer Vision
Aug 27
Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization
Zero-WAM introduces a causal video-action model that enables robots to perform unseen manipulation tasks by following in-context human video guidance. The authors create HumanGen, a dataset of 74.2K human-robot ICL pairs across 8.6K tasks, and propose an in-context future chunk prediction objective to prevent shortcut learning. In simulation, Zero-WAM attains a 47.0% success rate on seven unseen tasks, outperforming the best video-action baseline by 29.5 percentage points, and demonstrates real‑world generalization to complex, long‑horizon, and fine‑grained tasks.
arXiv AI
Jun 9
CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control
arXiv:2606. 09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution.